{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Gradient Boost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='auto',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the model\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Fitting and training\n",
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.10      0.20      0.13         5\n",
      "         Buy       0.33      0.22      0.27         9\n",
      "        Hold       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.18        17\n",
      "   macro avg       0.14      0.14      0.13        17\n",
      "weighted avg       0.21      0.18      0.18        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'n_estimators': [50,100, 200],\n",
    "          'learning_rate': [1,0.1,.01],\n",
    "          'max_depth': [1, 3, 6],\n",
    "          'min_samples_split': [2, 5],\n",
    "          'min_samples_leaf': [1, 3, 5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 162 candidates, totalling 486 fits\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.373), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.505), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.319), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.263), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.583), total=   0.4s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.319), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.263), total=   0.7s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.583), total=   0.5s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.319), total=   2.0s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.373), total=   0.5s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.505), total=   0.4s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.319), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.263), total=   0.6s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.611), total=   0.6s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.319), total=   0.4s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.263), total=   1.3s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.583), total=   1.1s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.319), total=   0.5s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.265), total=   0.1s\n",
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    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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    },
    {
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      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
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      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
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      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=3, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.359), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[CV]  learning_rate=1, max_depth=3, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.250), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
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      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=3, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.286), total=   0.2s\n",
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    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
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      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.452), total=   0.4s\n",
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      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n"
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    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
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      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.310), total=   0.2s\n",
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.369), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.314), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.386), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.307), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.370), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.408), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.199), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=0.925, test=0.222), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=0.966, test=0.408), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=0.948, test=0.273), total=   0.2s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.290), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.547), total=   0.2s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=0.982, test=0.319), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.345), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.611), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.319), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=0.925, test=0.222), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=0.948, test=0.273), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=0.982, test=0.351), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.611), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.319), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50, score=(train=0.881, test=0.217), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50, score=(train=0.927, test=0.268), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=50, score=(train=0.913, test=0.273), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=100 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=100 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=100 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=200 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=2, n_estimators=200 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=5, n_estimators=50 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=5, n_estimators=50, score=(train=0.913, test=0.273), total=   0.1s\n",
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      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=0.982, test=0.273), total=   0.2s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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    },
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      "  'precision', 'predicted', average, warn_for)\n",
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    {
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.351), total=   0.4s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.262), total=   0.5s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n",
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      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
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      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.294), total=   1.0s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.328), total=   0.8s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50, score=(train=0.703, test=0.221), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50, score=(train=0.893, test=0.215), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50, score=(train=0.867, test=0.276), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n"
     ]
    },
    {
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     "output_type": "stream",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=0.916, test=0.221), total=   0.3s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=0.928, test=0.290), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=0.948, test=0.290), total=   0.3s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.256), total=   0.8s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.349), total=   1.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.431), total=   0.7s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.703, test=0.221), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.893, test=0.215), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.910, test=0.276), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.916, test=0.221), total=   0.3s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.928, test=0.290), total=   0.4s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.948, test=0.290), total=   0.3s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.256), total=   0.5s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.349), total=   0.6s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.431), total=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 486 out of 486 | elapsed:  2.4min finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
       "                                                  init=None, learning_rate=0.1,\n",
       "                                                  loss='deviance', max_depth=3,\n",
       "                                                  max_features=None,\n",
       "                                                  max_leaf_nodes=None,\n",
       "                                                  min_impurity_decrease=0.0,\n",
       "                                                  min_impurity_split=None,\n",
       "                                                  min_samples_leaf=1,\n",
       "                                                  min_samples_split=2,\n",
       "                                                  min_weight_fraction_leaf=0.0,\n",
       "                                                  n_estimators=100,\n",
       "                                                  n_iter_no...\n",
       "                                                  presort='auto',\n",
       "                                                  random_state=None,\n",
       "                                                  subsample=1.0, tol=0.0001,\n",
       "                                                  validation_fraction=0.1,\n",
       "                                                  verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'learning_rate': [1, 0.1, 0.01],\n",
       "                         'max_depth': [1, 3, 6], 'min_samples_leaf': [1, 3, 5],\n",
       "                         'min_samples_split': [2, 5],\n",
       "                         'n_estimators': [50, 100, 200]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.9411612615080922\n",
      "Mean Testing Score: 1.0\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 1,\n",
       " 'max_depth': 3,\n",
       " 'min_samples_leaf': 3,\n",
       " 'min_samples_split': 5,\n",
       " 'n_estimators': 50}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None, learning_rate=1,\n",
       "                           loss='deviance', max_depth=3, max_features=None,\n",
       "                           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                           min_impurity_split=None, min_samples_leaf=3,\n",
       "                           min_samples_split=5, min_weight_fraction_leaf=0.0,\n",
       "                           n_estimators=50, n_iter_no_change=None,\n",
       "                           presort='auto', random_state=None, subsample=1.0,\n",
       "                           tol=0.0001, validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.14      0.20      0.17         5\n",
      "         Buy       0.38      0.33      0.35         9\n",
      "        Hold       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.24        17\n",
      "   macro avg       0.17      0.18      0.17        17\n",
      "weighted avg       0.24      0.24      0.24        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
